Modeling multiple response processes in judgment and choice.

Abstract

In this article, I show how item response models can be used to capture multiple response processes in psychological applications. Intuitive and analytical responses, agree–disagree answers, response refusals, socially desirable responding, differential item functioning, and choices among multiple options are considered. In each of these cases, I show that the response processes can be measured via pseudoitems derived from the observed responses. The estimation of these models via standard software programs that allow for missing data is also discussed. The article concludes with two detailed applications that illustrate the prevalence of multiple response processes.

A Simulation Study on Methods of Correcting for the Effects of Extreme Response Style

“…Our results indicate that for both mixed and multidimensional data, which make widely differing assumptions regarding the nature of response styles, trait recovery is best when a 2-dimensional model is applied. Other methods that have been proposed for the modeling of response styles but that were not considered in this study include Böckenholt (2012), who developed a model that divides the response process into subprocesses related to the trait and subprocesses related to response styles, and Rossi, Gilula, and Allenby (2001), who developed a Bayesian hierarchical model for modeling response styles. Thus, future research could investigate trait recovery in these models and multidimensional models based on alternatives to the PCM such as the graded response model.…”

A generalized item response tree model for psychological assessments

“…De and Boeckenholt (2012) presented item response models that are based on a tree structure and allow for multiple sources of individual differences. These item response tree models can be specified as special cases of the proposed generalized item response tree models.…”

“…The leaves are the terminal nodes that represent the observed categorical item responses. The model will be referred to as an item response tree model due to its utilization of the tree structure (e.g., Boeckenholt, 2012;. Figure 1 illustrates four tree structures that can be used to represent different cognitive processes for an item with four response categories (numbered from 1 to 4).…”

Item Response Modeling of Multivariate Count Data With Zero Inflation, Maximum Inflation, and Heaping

“…Bolt and Newton (2011) describe a multidimensional nominal response model (NRM) in which there is a latent variable related to the construct of interest as well as a latent variable for ERS. Others have approached the topic from a decision tree perspective, in which the observed responses manifest from a sequence of internal decisions (Böckenholt, 2012;De Boeck & Partchev, 2012;Thissen-Roe & Thissen, 2013). Böckenholt (2012) and De Boeck and Partchev (2012) propose a tree structure for capturing individual differences in response style, arguing that it is possible that more than one response process is at play when someone responds to a Likert-type questionnaire item.…”

“…Simple modifications to a conventional IRT model are not likely to account for the potential subpopulations and individual differences that manifest in Figure 1. This research attempts to address the challenges of modeling multivariate count data with inflation and heaping by combining methodological approaches from three distinct but related literatures: IRT models for multivariate count data (L. Wang, 2010), latent variable models for heaping (H. Wang & Heitjan, 2008) and extreme responding (Böckenholt, 2012;Bolt & Johnson, 2009;De Boeck & Partchev, 2012;Thissen-Roe & Thissen, 2013), and mixture IRT models (Finch & Pierson, 2011;Finkelman, Green, Gruber, & Zaslavsky, 2011;Sawatzky, Ratner, Kopec, & Zumbo, 2012;Wall, …”